Calculate the ROI on Infrastructure Automation

Programmable infrastructure and a world where you can take your data with you wherever is the future.

A new era has arrived, one in which software development practices are being applied to physical objects such as roads or bridges for greater efficiency; this idea of transparent skies could very well become a reality soon!

You can’t put a price on efficiency. The return on investment (ROI) of infrastructure automation is essential to consider before you start implementing changes that could be expensive and time-consuming.

How is Automation Valued?

The process of automating a long, manual task can be very beneficial. If you run it frequently enough and with the right system, your savings will grow exponentially as time goes on!

Regarding infrastructure automation, one of the first questions businesses ask is, “What’s the ROI?” In other words, what are the tangible benefits of automating tasks such as provisioning, configuration management, and deployments? And more importantly, how can we quantify those benefits?

In this blog post, we’ll walk you through a simple process for calculating the ROI of infrastructure automation. By the end, you’ll have a clear understanding of the financial benefits of automation and be able to make a strong case for why your business should invest in it.

The Advantages of Infrastructure Automation

Infrastructure automation is the process of automating IT infrastructure configuration, provisioning, and management. It can help organisations to manage their infrastructure more efficiently, improve service quality, and reduce operational costs. In this blog post, we will explore some of the main advantages of infrastructure automation. 

  • Improved Efficiency and Productivity

Infrastructure automation can improve efficiency and productivity. By automating configuration, provisioning, and management tasks, organisations can free up time for other activities, such as developing new features or products and providing customer support. These tasks can reduce errors and improve accuracy.

  • Improved Service Quality

Another advantage of infrastructure automation is that it can improve service quality. By automating tasks such as monitoring and maintenance, organisations can ensure their infrastructure is always running smoothly and efficiently. Additionally, automating these tasks can help identify problems early before they cause significant disruptions. Organisations can provide better service to customers.

  • Reduced Operational Costs

Finally, another advantage of infrastructure automation is that it can help to reduce operational costs. This is because automating tasks such as provisioning and management can help to reduce the need for manual intervention. Additionally, automating these tasks can help improve efficiency and productivity, which can lead to reduced labour costs. In addition, automating these tasks can also lead to reduced energy consumption and waste generation. As a result, organisations can save money on their operating costs. 

There are many advantages of infrastructure automation. Automating tasks such as configuration, provisioning, and management can help to improve efficiency and productivity, improve service quality, and reduce operational costs. If you are considering implementing infrastructure automation in your organisation, carefully weigh all of these factors to make the best decision for your business.

Calculating the ROI of Infrastructure Automation

Now that we’ve looked at some of the benefits of infrastructure automation let’s talk about how you can calculate its ROI. To do this, we’ll use a simple formula:

 (Total savings from automation – Cost of automation) / Cost of automation = ROI%

For example, you spend $5,000 per month on labour to manually provision and manage your servers. You estimate that by investing in an automated system, you could reduce that cost by 50%. The cost of the computerised system itself is $10,000 upfront plus $500 per month in maintenance costs. Using our formula, we get: 

 ($5,000 * 0.5 – $10,000 – $500) / ($10,000 + $500) = -9%

This means that over two years—the amount of time it would take to fully recoup your upfront investment—you would see a negative return on your investment (ROI). In other words, investing in automation wouldn’t make financial sense for your business now.

Conclusion:

As you can see from our example above, calculating the ROI of infrastructure automation is relatively simple. However, it’s important to note that other factors besides financial ones should be considered when deciding whether or not to automate your infrastructure. These include the size and complexity of your infrastructure, your company’s culture, and your willingness to embrace change. That said, we hope this blog post has given you a better understanding of how to calculate the ROI of infrastructure automation and why it’s such an important consideration for businesses today. Thanks for reading!

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Redefining Business Process Outsourcing through Business Process Automation

The business process outsourcing (BPO) industry is worth an estimated $190 billion. But what is BPO, and how has it evolved? In this blog post, we’ll explore the history of BPO and how business process automation (BPA) is redefining the industry.

Business Process Outsourcing: An Introduction

Business process outsourcing (BPO) has been a popular cost-cutting measure for businesses for many years. The concept is simple enough: rather than having an in-house team handle a specific process or task, you outsource it to a third-party provider. This often results in significant cost savings, as BPO providers can leverage economies of scale to deliver services at a lower cost than most businesses could achieve on their own.

However, new development is beginning to change the BPO landscape: business process automation (BPA). BPA involves using technology to automate tasks previously performed by human workers. This includes data entry, customer service, and even complex financial processes.

The History of Business Process Outsourcing

BPO had its roots in the late 1800s when American companies began outsourcing manual labour to countries with lower wages, such as China and India. This practice continued into the 20th century with the rise of telephone operators and data entry clerks. However, it wasn’t until the 1990s that BPO began to take off. 

This was primarily due to technological advances that allowed communication and collaboration across vast distances. Suddenly, businesses could outsource not just manual labour but also knowledge work to countries with lower living costs. This led to the rise of call centres and other forms of customer service outsourcing.

In recent years, there has been a shift away from traditional BPO models. This is due to several factors, including the increasing cost of labour in countries like China and India, as well as the advent of new technologies that make it possible to automate many business processes.

Benefits of BPA 

There are several benefits that businesses can reap from implementing BPA: 

  • Cost savings: One of the primary benefits of BPA is that it can help businesses save money. Companies can reduce their labour costs by automating tasks that human workers previously performed. In some cases, BPA can also help enterprises improve their efficiency and reduce other costs, such as errors and rework.
  • Improved quality: Another benefit of BPA is that it can help improve the quality of work. BPA is designed to follow pre-determined rules and procedures. This contrasts with human workers, who may make mistakes or take shortcuts that result in lower-quality work.
  • Increased capacity: A final benefit of BPA is that it can help businesses increase their capacity without incurring additional costs. This is because BPAs can work faster and for longer hours than human workers. In some cases, this increased capacity can help businesses meet spikes in demand or complete time-sensitive tasks more quickly. 

Implementing BPA

If you’re interested in implementing BPA within your business, there are a few things you’ll need to do:

  1. Determine which processes you want to automate: The first step is to evaluate your business processes and determine which ones would be candidates for automation. To do this, you’ll need to consider factors such as the complexity of the process, the frequency with which it needs to be performed, and the availability of data and applications required to support it.
  2. Identify the right tools: Once you’ve identified which processes you want to automate, you’ll need to select the right tools. There are many different types of BPAs available on the market today, so you’ll need to evaluate your needs and choose the tool best suited for your specific requirements.
  3. Define success criteria: Before beginning any automation project, it’s essential to define what success looks like. This will help you select an appropriate tool and set realistic expectations for the project’s outcomes.
  4. Implement and test: Once you’ve chosen an agency and defined success criteria, you’re ready to implement your BPA solution. Be sure to test it thoroughly before rolling it out into production to address any potential issues before they cause problems for your business operations.

Business Process Automation: The Future of BPO?

Business process automation (BPA) is the use of technology to automate repetitive, low-value tasks typically performed by human workers. BPA can be used to automate a wide variety of business processes, including data entry, invoice processing, and lead generation.

One of the critical benefits of BPA is that it can help businesses reduce their dependence on human labour. This is especially important in today’s economy, where many companies struggle to find enough qualified workers to fill open positions. By automating low-value tasks, companies can free their employees to focus on more strategic initiatives.

BPA is also more efficient and accurate than human workers, and machines can work 24 hours a day, 365 days a year, without getting tired or making mistakes. This increased efficiency can help businesses save money and increase profits.

Conclusion:

The business process outsourcing industry is evolving thanks to advances in technology rapidly. Business process automation is redefining what is possible in terms of outsourcing and helping businesses save money and increase efficiency in the process. In the future, we can only expect BPA to become more prevalent as companies continue to search for ways to cut costs and improve performance.

How to Embed Tableau or Power BI Dashboards into Web Pages without Using an Iframe

Iframe: An Introduction

Iframes are an HTML element that allows you to embed one HTML document inside another. While they are commonly used to embed videos or maps on websites, they can also be used to embed dashboards created in Tableau or Power BI. However, iframes can cause problems with security and website loading times, which is why some developers prefer to avoid using them. So, how can you embed a Tableau or Power BI dashboard on a webpage without using an iframe? Keep reading to find out.

Ways to Embed iFrame to your Web Pages

Many web developers shy away from using iframes because they can be difficult to work with. However, iframes are often the only way to embed Tableau or Power BI dashboards into web pages. If you’re looking for a way to embed your dashboards without using an iframe, read on!

Let us walk you through 3 methods for embedding Tableau or Power BI dashboards into web pages. These methods are:

1. Use Tableau’s or Power BI’s JavaScript API

2. Use a third-party service like Publitas

3. Use an open-source solution like Koalas

We’ll also provide a brief overview of each method so that you can decide which one is right for you. 

Method 1: Use Tableau’s or Power BI’s JavaScript API

  • If you’re aTableau or Power BI user, then you’re in luck! Both platforms offer a JavaScript API that allows you to embed your dashboards into web pages without using an iframe. 

The biggest advantage of using the JavaScript API is that it gives you full control over how your dashboard is rendered on the page. For example, you can choose to display the dashboard as a lightbox pop-up or inline within the page. You can also specify the size and position of the dashboard on the page. 

Another advantage of using the JavaScript API is that it’s relatively simple to set up and use. However, one downside is that it requires some knowledge of HTML and CSS in order to properly configure it. 

Method 2: Use a Third-Party Service like Publitas 

  • If you’re not a web developer and don’t have any knowledge of HTML or CSS, then using a third-party service like Publitas is probably your best bet. Publitas offers an easy-to-use platform that allows you to embed your Tableau or Power BI dashboards into web pages with just a few clicks. 

The biggest advantage of using Publitas is that it’s very user-friendly and doesn’t require any knowledge of HTML or CSS. Another advantage is that Publitas offers a wide range of customization options so that you can control how your dashboard looks on the page. 

However, there are some downsides to using Publitas. First off, it’s a paid service, so you’ll need to sign up for one of their subscription plans in order to use it. Additionally, because Publitas is a third-party service, there’s always the potential for compatibility issues between their platform and your dashboard software (e.g., Tableau or Power BI). 

Method 3: Use an Open-Source Solution like Koalas

  • Koalas is an open-source solution that allows you to embed Tableau or Power BI dashboards into web pages without using an iframe. The advantage of using Koalas is that it’s free to use and doesn’t require any knowledge of HTML or CSS. Additionally, Koalas offers a wide range of customization options so that you can control how your dashboard looks on the page. 

There are some downsides to using Koalas, however. First off, because it’s an open-source solution, there’s always the potential for compatibility issues between Koalas and your dashboard software (e.g., Tableau or Power BI). Additionally, Koalas doesn’t offer as many features as Publitas (e .g . , lightbox pop – ups), so keep that in mind when deciding which solution is right for you. 

Choosing the right method for embedding your Tableau or Power BI dashboard into a web page depends on several factors, including your level of technical expertise, budget, and desired features. We hope this blog post has helped you better understand your options so that you can make an informed decision about which method is right for you.

Conclusion: 

Iframes are commonly used to embed dashboards created in Tableau or Power BI onto websites. However, they can cause problems with security and website loading times. As such, some developers prefer to avoid using them altogether. Luckily, there are two methods that you can use to embed a Tableau or Power BI dashboard on a webpage without using an iframe. So, whether you’re a developer who wants more control over the code or someone who just wants an easy solution, there’s a method here for you.

3 Strategies for cloud enabling your business

As per IDG Cloud computing survey, 92% of organization’s IT environment is at least somewhat in the cloud today. If you are in those 8% who have not adopted cloud yet, you are clearly missing out on the benefits that other organizations are getting. By now, I am sure you know the most benefits that Cloud infrastructure offers, but let’s refresh it once more –

  1. More Resiliency – The cloud providers scale up and down as per your seasonal needs. It is the most ideal way to support your growth, as you don’t need to purchase and hold assets for a peak requirement, but you can adjust as you go along.
  2. Less Headache – You don’t need to keep an army of people to upkeep your servers, manage security and hardware failures. You can sleep well knowing that the cloud providers are well-equipped in security and disaster recovery capabilities to ensure safekeeping of your assets and data. You, of course, need help to plan, configure and monitor it, but once you get the setup right, it is a smooth journey.
  3. Access to new capabilities – The cloud providers offer advanced capabilities and services that make your life easier. With a competitive market, the more and more services are being made available and the cost is going down. In 2021 AWS: reinvent, Amazon announced more than 50 new services on the top a very mature array of services. You can access these capabilities with a click of button, right from document storage to artificial intelligence, and create growth opportunities for your business.
  4. Potentially less cost – I am being careful here as many organizations have seen increased monthly spend when they compare with their current ‘infrastructure’ costs. However, if you look at it holistically by adding people/process/tools costs of running your current (and future business), the cloud costs will be potentially much lesser.

If you are convinced and are thinking of adopting cloud for your business, what should be your strategy for adoption?

There are following three popular strategies that our customers have adopted –

  1. Lift and Shift

    If you have a portfolio of applications on your premise, the fastest way to address your ‘cloud adoption’ business case is by lifting and shifting your applications cloud. It makes sense if you are going to get rid of your data center (or hosting provider) and save money on operations by migrating to cloud.
    Although you can do it manually by evaluating every application, for a portfolio of applications, tool-based migration process is preferred. There is a very strong tooling ecosystem for AWS, including its own Cloud endure ( now AWS MGN), to support the migration. The tools can help you right from ‘Discovery and Planning’, ‘Creating a business case’, Migration and also post-migration ‘Monitoring and Support’.

  2. Fit and Shift

    It is my favorite option as it lets you leverage the new capabilities that Cloud offers while preserving the value of your legacy investments. It includes selectively replacing components of your current application by ‘cloud managed’ components. For example, you can swap an Oracle database for AWS RDS, and reduce your license costs and maintenance efforts. As you are developing a strategy by assessing every application, it takes a longer time (~8-12 weeks for 30 applications) to define your migration strategy, but your potential benefits are likely to be much higher than just ‘lifting and shifting’ to cloud.

  3. Don’t Shift, Go Native

    If you are convinced that your legacy is not going to support your future growth, it is better to re-architect your applications and re-develop using cloud native capabilities. The cloud provides many platform services to rapidly build and deploy a ‘cloud-native’ application, so it may not be overly expensive option. For a global association of risk professionals, we recently re-developed their mission-critical financial risk estimation application within just 2 months, by reusing their existing python based code and leveraging AWS serverless and RDS services. This approach not only saved the development and maintenance costs, but also provided a scalable solution to onboard new associates very easily.

In conclusion, the cloud provides significant benefits to your business and there are proven strategies to adopt the cloud. Futran, in partnership with AWS, helps organizations to define the migration strategies and adopt it in cost-effective, risk-free way. Please contact a Futran Cloud Specialist if you want to know more about how we can help.

The Big Data Game: How Data is Causing Major Metamorphosis in The Insurance Industry

Today, data is an asset that can be used both internally and externally. Companies can collect more information than ever before in a variety of ways. Digital interactions with customers on mobile devices and stationery allow us to track interactions and requests.

The Internet of Things (IoT) has provided billions of connected devices and objects equipped with tools to measure, record and communicate information about purchases, customer responses to advertising campaigns and marketing messages that can be captured and analyzed.

Accurate information requires data analysis programs that can easily collect, store, analyze, display, and report on information from a variety of sources. These insights provide business leaders with the information they need in real-time to make better decisions.

The Data-Backed Change

With converting client expectancies and the sheer expanse of data being collected, insurance businesses are already converting their models and processes.

Among the high-quality modifications are the following:

  • Investment in tools to better understand their customers, their expectancies and their needs
  • Development of recent insurance alternatives to provide guidelines which are shielding extra items
  • Responding to an ageing populace whose insurance needs are converting in massive numbers and quickly
  • Realtime processing and mobile apps that respond to the expectancies of younger customers, who anticipate a digital-first approach (this pace and responsiveness are often contrary to the traditional approaches insurers had in the past)
  • Using new technology inclusive of blockchain, artificial intelligence, machine learning, and the IoT to create better operational efficiencies and create more connections with objects and customers

Challenges faced by insurers in leveraging data to drive digital transformation

Digital transformation provides a fantastic possibility for insurance companies. However, the same companies may also face limitations that delay the utilization and leveraging of data.

Among those problems are:

Lack of Policies and Procedures: 

Many insurance companies have grown because of acquisitions, which means that the integration of systems and policies is challenging.

The data-driven insurance company needs:

  • Data governance structures and policies
  • Consistent data definitions
  • Clarity of data ownership
  • Standards for the collection, storage and use of data
  • Data security guidelines

Cultural and Organizational Roadblocks: 

An insurance company that desires to achieve success in the use of data in new approaches needs to address these concerns promptly:

  • Internal cultural variations in how data is seen, valued and used
  • Data stored in silos without standardization and a single source of information
  • Complex data structures
  • Inconsistent data formatting
  • Reluctance to share data internally
  • Data quality and more than one kind and sources for data (structured, unstructured, collected, purchased)

Technological Barriers:

Technology is a common barrier or leveraging data. Challenges include:

  • Data stored both in the cloud and on-premises
  • Legacy systems, often highly customized
  • Lack of certified internal IT resources

Leverage data with Futran  

Here are some recommendations for optimizing the opportunity for digital transformation for Insurance companies.

  • Invest in Data Analytics – Big data analysis tools enable insurers to collect and use data from multiple sources at the same time, identify patterns, better detect fraud, and resolve cases faster.
  • Using advanced OCR software – The insurance process still relies on paper, either that it generates or that comes from other sources. Sound OCR software reduces manual entry and re-entry, promotes better storage, retrieval and analysis of unstructured data while accelerating processes.
  • Improves two-way communication – Insurers need to better engage with their customers — apps, content, and messaging tools. Tools should be primarily mobile and include features to capture interactions and outcomes.
  • Use AI to improve engagement – Artificial intelligence tools like virtual assistants can provide customers with the information or live assistance they need, saving resources and fixing routing issues.
  • Partners for Transformation – Insurers need strategic partners to help them collect, analyze and use data for digital transformation.

Conclusion

Emerging Leaders in the insurance industry are using insurance data analytics to manage risk selection and pricing strategy decisions. New-gen technology is working progressively to implement regulatory approaches to understand big data in a variety of insurance transactions such as underwriting, claims management, customer satisfaction and policy management to provide better predictive analytics. This allows insurance companies to describe the analytical decision-making process in all of their internal processes and business operations.

Futran helps insurance companies with their digital transformations by providing the best Data Analytics solutions such as Cloud-Based Data Warehouse & Data-Lake, Data Management, Managed Services & Analytics Model Development.

Six Top Data Management Practices Every Organization Must Follow

Six Top Data Management Practices Every Organization Must Follow

Storage silos in most traditional organizations are bursting open from the rapid evolution in big data. Most of these organizations are now concerned about data management practices in their organizations.

In the last decade or so, every industry from manufacturing to advertising has migrated to multichannel sourcing of data. This means each individual set of data now competes with every other set for analytical significance. Businesses can easily stretch out of their means of trying to fuel this process. Resultantly, very few companies can claim that they are making the best use of their data.

By all means, the answer lies in implementing a data management solution that is practical. Plus, it should improve the quality of the collected data. Moreover, it can also be a vital step toward solving productivity issues.

The focus is steadily shifting toward the production of well-analyzed, relevant and timely data. Such data allows businesses to make improved decisions and usher in substantial growth. Fitting in data management solutions in business could be challenging. And if you have not started yet, you might totally miss out on what’s actually covered in data management.

With a data management plan that is centered on specific business needs, every new asset in data will undergo extensive monitoring processes to make sure there are no security threats and data is kept safe. Here are some top data management principles and practices that will help your organization make the most of the available data assets.

Understand your business goals before data objectives

Over the next decade or so, the volume of data will snowball into a living data giant. In parts, this development will be propelled by the new digital devices that are constantly being added to systems and networks. The uninterrupted flow slows down data collected previously further down the silos as newer sets of data assume more importance.

Using data to understand and realize business goals is quite common as a practice. But a data would scientist would recommend that organizations keep referring to the business goals throughout the process of data planning. This helps companies identify the most important data sets and understand whether or not those need to be placed in a silo.

As an organization, you also need to consider how every dataset can impact the KPI that you would want to improve. Based on the goal you set, you will have to make a decision on what data you want to store. At the moment, most organizations do shoddy data management.  They store a lot of data without a well-defined purpose or store mechanism.

The best way to work around this is to know and decide how much data and associated technologies you will need to crack the goal.

Club AI and machine learning in data management

The more datasets an organization accrues, the more time it takes to conduct analysis and reporting on every one of them. With new techniques like artificial intelligence, the extraction levels on the collected datasets are all set to go deeper with machines getting contributing a bigger chunk in the analysis. Data companies are already championing inter-technology collaboration to better facilitate GDPR guidelines.

The other big factor in data management is big data. Given how big data has become in the past few years, artificial intelligence will be an even bigger factor in the months and years to follow. AI can deliver fast, economical and high-quality intelligence from ginormous sets of data. It is beyond impossible for humans to derive actionable insights from such data volumes.

With the onset of GDPR, almost any organization that dealing in significantly large volumes of data will need artificial intelligence. The major ways in which AI will help companies in better data keeping include:

  • The ability for consumers to check in and out of official communications
  • Supply consumers with reports on what data the company collects from them
  • Give consumers easy ways to delete all data the company has about them

Without artificial intelligence supplying the necessary technology, these processes will become heavily time-consuming for businesses.

Ensure the right people manage data

A good data strategy for a business starts with placing the best practices and principles in place. However, what you want to know is that success is a result of the right people managing data for your organization.

Start with planned data governance. Deriving maximum value from data is critical to any data strategy of a business. Perhaps, the first of many steps in data strategy is to include data governance as a principle. For one, this will make sure that the data being used in the business continues to stay of the highest quality throughout its lifecycle.

Data governance is a process in the evolution of new businesses. Since it’s based on integrity, usability, and availability, it allows for the whole industry to make use of the data. With big data and analytics, companies can improve security, reduce costs, ensure compliance, improve data quality and derive meaningful insight.

Implementing an enterprise-wide governance framework to reduce the cost of operation and risk associated in the subsequent projects.

Make data accessible

Data security is as important for an SME as it is for a Fortune 500 company. But in a mad bid to secure data, companies cannot afford to forget data, which might, in the long run, make it defunct altogether. Data needs to be stored securely, but without compromising on the accessibility for those who need the data. Imperatively, the same data should not be available to those who do not have the proper clearance.

Staying on the top of data access protocols is key to cope up with the rapid leaps into the digital age. Organizations must make sure that data is stored at places where relevant groups can have easy access to them. The age that is coming is more data-driven than we would think. At that, it is relevant that organizations are adequately prepared to extract data from dashboards. The message here is simple – silo data is not of any particular use to a company.

Defend cybersecurity threats

Most companies have an Incident Response Plan by now. But the common mistake that most companies end up making is to deviate from that plan. So first of all, there has to be a clear plan accentuated with decision points in times of crisis. That will let companies know if there is a legal requirement, good faith or regulation to find a breach which is either potential or realized.

To start with, an Incident Response Plan should be established before the occurrence of any major incident. The plan should include all the points that will help in recovery, eradication, containment and also supply with expert testimony.

Democratize data management 

Data management principles and practices must be kept up collectively by a business. Using a holistic method to work lets every member in the company to gain access to data infrastructure and create a way for better data management processes. Along with solid governance, this method can introduce successful master management of data as well. But for company-wide success, the integration must first happen within the company.

Data management practice aids in the study of data in the correct perspective to arrive at conclusions that align with business objectives. Now that organizations are hoarding lots and lots of data, the key is in classifying the data well and making a senior official in the company accountable for it.

Data democratization is desirable. There’s no question about that. However, it has surpassed desirability. With GDPR rolling into action faster than most would have imagined, someone within an organization has to take responsibility for the data of their users. Moreover, implementing stricter data guidelines will also ensure that companies are aware of the kind of data that flows through their organization.

If you follow these recommended data practices, you will be that much closer to making holistic use of data.

Futran Solutions specializes in delivering composite data management and analytics for small and medium enterprises. As applications of data management in business keep evolving, so do the resources that shoulder these needs within an organization. Speak to a Futran Data Analytics specialist today. Find out how we help you achieve your business and marketing objectives.

Seven Hottest Analytics And Big Data Trends For 2019

The Big data is the vast volumes of data generated from a number of industry domains. Big data generally comprises data collection, data analysis and data implementation processes. Through the years, there’s been a change in the big data analytics trends – businesses have swapped the tedious departmental approach with data approach. This has seen greater use of agile technologies along with heightened demand for advanced analytics. Staying ahead of the competition now requires businesses to deploy advanced data-driven analytics.

When it first came into the picture, big data was essentially deployed by bigger companies that could afford the technology when it was expensive. At present, the scope of big data has changed to the extent that enterprises both small and large rely on big data for intelligent analytics and business insights. This has resulted in the evolution of big data sciences at a really fast pace. The most pertinent example of this growth is the cloud which has let even small businesses take advantage of the latest technology.

The modern business is floating on a stream of never-ending information. However, most businesses face the challenge of extracting actionable insights from vast pools of unstructured data. Despite these roadblocks, businesses are deriving from the tremendous opportunities for growth presented by big data. Here is all that would count as the hottest big data analytics trends of 2019.

Booming IoT Networks

Big-Data-Trends-2

Like it’s been through 2018, Internet of Things (IoT) will continue to trend through 2019, with annual revenues reaching way beyond $300 billion by 2020. The latest research reports indicate that the IoT market will grow at a 28.5% CAGR. Organizations will depend on more structured data points to gather information and gain sharper business insights.

Quantum Computing

 

Industry insiders believe that the future of tech belongs to the company that builds the first quantum computer. No surprise that every tech giant including Microsoft, Intel, Google and IBM are racing for the top spot in quantum computing. So, what’s the big draw with quantum computing? It allows seamless encryption of data, weather prediction, solutions to long-standing medical problems and then some more. Quantum computing allows real conversations between customers and organizations. There’s also the promise of revamped financial modeling that helps organizations develop quantum computing components along with applications and algorithms.

Analytics based on Superior Predictive Capacity

 

More and more organizations are using predictive analysis to offer better and more customized insights. This, in turn, generates new responses from customers and promotes cross-selling opportunities. Predictive analysis helps technology seamlessly integrate into variegated domains like healthcare, finance, aerospace, hospitality, retailing, manufacturing and pharmaceuticals.

Edge Computing

 

The concept of edge computing among other big data trends did not just evolve yesterday. Network performance streaming makes use of edge computing pretty regularly even today. To save data on the local server close to the data source, we depend on the network bandwidth. That’s made possible with edge computing. Edge computing stores data nearer to the end users and farther from the silo setup with the processing happening either in the device or in the data center. Naturally, the entire procedure will see an organic growth in 2019.

Unstructured or Dark Data

 

Dark data refers to any data that is essentially not a part of business analysis. These packets of data come from a multitude of digital network operations which are not used to gather insights or make decisions. Since data and analytics are increasingly becoming larger parts of the daily aspects of our organizations, there’s something that we all must understand. Losing an opportunity to study unexplored data is a big-time potential security risk.

More Chief Data Officers

 

The latest trendy job role on the market is that of a Chief Data Officer. Top-tier human resource professionals are looking for competent industry professionals to fill this spot. While the demand is quite high, the concept and value of a CDO are largely still undefined. Ideally, organizations are preferring professional with knowledge in data analysis, data cleaning, intelligent insights and visualization.

Another Big Year for Open Sourcing

 

Individual micro-niche developers will invariably step up their game in 2019. That means we will see more and more software tools and free data become available on the cloud. This will hugely benefit small organizations and startups in 2019. More languages and platforms like the GNU project, R, will hog the tech limelight in the year to come. The open source wave will definitely help small organizations cut down on expensive custom development.

Making of a Storm: What Happens to Dark Data in Analytics and Big Data?

Making of a Storm: What Happens to Dark Data in Analytics and Big Data?

Dark data is the kind of data that does not become a part of the decision making for organizations. This is generally the data from logs and sensors and other kinds of transactional records which are available but generally ignored. The largest portion of the yearly big data collected by organizations is also dark data.    

Dark data does not usually play a vital role in analytics because:

  1. Companies do not want to use their bandwidth on additional data processing
  2. There’s a lack of technical resources
  3. Organizations do not believe dark data adds any value to their analytics

All of these are valid reasons for the data taking the back seat. But today we have a string of data-centric technological advances. Together, they present a heightened ability to ingest, source, analyze, and store large volumes of data. With that, it becomes important for organizations to recognize this largely untapped volume of data.   

The conventional way to use this data would be to systematically drain all of it into a waterhouse of data. This is followed by the identification, reconciliation, and rationalization of the data. The reporting follows soon after. While the process is pretty methodical, there might not be as many projects that truly call for such a need.   

The Immense Volume of Dark Data in Enterprise

Dark Data Big Data Analytics 2

At the moment, we have solid  evidence to suggest that as much as 90% of all data used in enterprises could be dark. Since industries are now storing large data volumes in the ‘lake’, it should be natural to tag the data appropriately as it gets stored. Perhaps the key is to extract the metadata out of this data and then storing it. 

Profiling and exploring the data can be done using one or a combination of tools that are already available in the market. Cognitive computing and machine learning can further increase processing power and open up possibilities of making intelligent use of dark data.  

Dark data may or may not have an identifiable structure. For example, most contacts and reports in organizations are structured. But over the course of time, they add up to the pile of dark data. Unstructured data can be small bits of personally identifiable info like birth dates and billing details. In the very recent past, this type of data would remain dark.

Machine learning can help organize this data in an automated manner. It can then be connected to other attributes of data to generate the complete view of the data. Using geolocation data is slightly trickier though. While it is extremely valuable, the lifespan is rather short. A collection of historical geolocation data sets can be further leveraged using machine learning to aid in predictive analysis of data.    

Recognition of regular data as dark data

Other sets of data often considered “dark” in the past include data from sensors, logs, emails, and even voice transcripts. The longest stretch they would get in terms of application would be vested in troubleshooting purposes. Not many would look to make such data a part of actual decision making. Now that we can convert voice or text (and vice versa) and use the data to gather intelligence, there are many use cases that draw advantage of data traditionally considered dark.    

An IDC estimate suggests that the total volume of data could be somewhere close to 44ZB (zettabytes) in 2020. This data explosion will be influenced by many new data generators like the Internet of Things. And unless we light up this data with new technology and processes, a large volume of it will continue to stay dark.  

The first and obvious step will be to make all the dark data available for exploration. The second step is to categorize the data, scrape out the metadata and do a quality check for all the extracted data. Modern tools for data management and data visualization provide the ability to explore the data visually. This determines whether or not the data can be illuminated to remove the visual noise.      

The myriad advances in Artificial Intelligence (AI) will definitely aid in uncovering the secrets of the oft-ignored “dark data”. However, the trick is still in using the data prudently. Wrong use of data will inadvertently result in incorrect predictions and may invite regulatory sanctions.

The vastness of dark data demands handling by Big Data and AI experts. In addition, there needs to be a clear plan about the application of the data once it is sorted. At Futran Solutions, we work with a pool of incredibly talented Big Data and Artificial Intelligence experts who can help your organization make the most of dark data. Contact us today to talk solutions in big data and artificial intelligence.